24 October 2017 Bayesian multi-frame super-resolution of differently exposed images
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Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 104620B (2017) https://doi.org/10.1117/12.2281556
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Abstract
This paper presents a technique that performs multi-frame super-resolution of differently exposed images. The method first employs a coarse-to-fine image registration method to align image in both spatial and range domain. Then an image fusion method based on the maximum a posterior (MAP) is used to reconstruct a high-resolution image. The MAP cost function includes a data fidelity term and a regularized term. The data fidelity term is in the L2 norm, and the regularized term employs Huber-Markov prior which can reduce the noise and artifacts while reserving image edges. In order to reduce the influence of registration errors, the high-resolution image estimate and registration parameters are refined alternatively by minimizing the cost function. Experiments with synthetic and real images show that the photometric registration reduce the grid-like artifacts in the reconstructed high-resolution image, and the proposed multi-frame super resolution method has a better performance than the interpolation-based method with lower RMSE and less artifacts.
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Jieping Xu, Yonghui Liang, Jin Liu, Zongfu Huang, Xuewen Liu, "Bayesian multi-frame super-resolution of differently exposed images", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104620B (24 October 2017); doi: 10.1117/12.2281556; https://doi.org/10.1117/12.2281556
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